In Situ Comparison of Three Dielectric Soil Moisture Sensors in Drip Irrigated Sandy Soils

2005 ◽  
Vol 4 (4) ◽  
pp. 1037-1047 ◽  
Author(s):  
Finn Plauborg ◽  
Bo V. Iversen ◽  
Poul E. Laerke
Keyword(s):  
Soil Moisture ◽  
Sandy Soils ◽  
Soil Moisture Sensors

scholarly journals Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

2015 ◽  
Vol 16 (2) ◽  
pp. 889-903 ◽  
Author(s):  
Wesley J. Rondinelli ◽  
Brian K. Hornbuckle ◽  
Jason C. Patton ◽  
Michael H. Cosh ◽  
Victoria A. Walker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Soil Drying ◽  
Surface Soil Moisture ◽  
South Fork ◽  
The South ◽  
Near Surface ◽  
Soil Moisture Sensors ◽  
Smos Satellite

Abstract Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

scholarly journals Approaches to calibrate in-situ capacitance soil moisture sensors and some of their implications

2016 ◽  
Author(s):  
N. A. L. Archer ◽  
B. R. Rawlins ◽  
B. P. Machant ◽  
J. D. Mackay ◽  
P. I. Meldrum
Keyword(s):  
Soil Moisture ◽  
Field Measurements ◽  
Soil Cores ◽  
Undisturbed Soil ◽  
Soil Moisture Sensors ◽  
Soil Sensors ◽  
Laboratory Calibration ◽  
Rain Events ◽  
Undisturbed Soil Cores

Abstract. Capacitance probes are increasingly being used to monitor volumetric water content (VWC) in field conditions and are provided with in-built factory calibrations so they can be deployed at a field site without the requirement for local calibration. These calibrations may not always have acceptable accuracy and therefore to improve the accuracy of such calibrations soil-specific laboratory or field calibrations are required. In some cases, manufacturers suggest calibration is undertaken on soil in which the structure has been removed (through sieving or grinding), whilst in other cases manufacturers suggest structure may be retained. The objectives of this investigation were to (i) demonstrate the differences in laboratory calibration of the sensors using both structured and unstructured soils, (ii) compare moisture contents at a range of suctions with those predicted from soil moisture release curves for their texture classes (iii) compare the magnitude of errors for field measurements of soil moisture based on the original factory calibrations and the laboratory-based calibrations using structured soil. Grinding and sieving clay soils to  50 % water to the ground and sieved soil samples, dielectric values to VWC > 50 % were observed to be significantly lower than using undisturbed soil cores taken from the field and therefore undisturbed soil cores were considered to be better to calibrate capacitance probes. Generic factory calibrations for most soil sensors have a range of measurement from 0 to 50 %, which is not appropriate for the studied clay-rich soil, where ponding can occur during persistent rain events, which are common in temperate regions.

scholarly journals Performance of Soil Moisture Sensors in Florida Sandy Soils

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 358 ◽  
Author(s):  
Rhuanito Soranz Ferrarezi ◽  
Thiago Assis Rodrigues Nogueira ◽  
Sara Gabriela Cornejo Zepeda
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Water Content ◽  
Sandy Soils ◽  
Irrigation Scheduling ◽  
Management Efficiency ◽  
Soil Moisture Sensors ◽  
Citrus Production ◽  
Gravimetric Water Content ◽  
Calibration Equations

Soil moisture sensors can improve water management efficiency by measuring soil volumetric water content (θv) in real time. Soil-specific calibration equations used to calculate θv can increase sensor accuracy. A laboratory study was conducted to evaluate the performance of several commercial sensors and to establish soil-specific calibration equations for different soil types. We tested five Florida sandy soils used for citrus production (Pineda, Riviera, Astatula, Candler, and Immokalee) divided into two depths (0.0–0.3 and 0.3–0.6 m). Readings were taken using twelve commercial sensors (CS650, CS616, CS655 (Campbell Scientific), GS3, 10HS, 5TE, GS1 (Meter), TDT-ACC-SEN-SDI, TDR315, TDR315S, TDR135L (Acclima), and Hydra Probe (Stevens)) connected to a datalogger (CR1000X; Campbell Scientific). Known amounts of water were added incrementally to obtain a broad range of θv. Small 450 cm3 samples were taken to determine the gravimetric water content and calculate the θv used to obtain the soil-specific calibration equations. Results indicated that factory-supplied calibration equations performed well for some sensors in sandy soils, especially 5TE, TDR315L, and GS1 (R2 = 0.92) but not for others (10HS, GS3, and Hydra Probe). Soil-specific calibrations from this study resulted in accuracy expressed as root mean square error (RMSE) ranging from 0.018 to 0.030 m3 m−3 for 5TE, CS616, CS650, CS655, GS1, Hydra Probe, TDR310S, TDR315, TDR315L, and TDT-ACC-SEN-SDI, while lower accuracies were found for 10HS (0.129 m3 m−3) and GS3 (0.054 m3 m−3). This study provided soil-specific calibration equations to increase the accuracy of commercial soil moisture sensors to facilitate irrigation scheduling and water management in Florida sandy soils used for citrus production.

scholarly journals Accounting for Temperature Effects on the Performance of Soil Moisture Sensors in Sandy Soils

2019 ◽  
Vol 83 (5) ◽  
pp. 1319-1323 ◽  
Author(s):  
Michelle Schwartz ◽  
Zhen Li ◽  
Toshihiro Sakaki ◽  
Ali Moradi ◽  
Kathleen Smits
Keyword(s):  
Soil Moisture ◽  
Temperature Effects ◽  
Sandy Soils ◽  
Soil Moisture Sensors

GNSS-based remote sensing: Innovative observation of key hydrological parameters in the Central Andes

2020 ◽  
Author(s):  
Nikolaos Antonoglou ◽  
Bodo Bookhagen ◽  
Danilo Dadamia ◽  
Alejandro de la Torre ◽  
Jens Wickert
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Central Andes ◽  
Time Domain Reflectometry ◽  
Precipitation Event ◽  
Hydrological Parameters ◽  
Soil Moisture Sensors ◽  
Gnss Receivers ◽  
Sar Data

<p>The Central Andes are characterized by a steep climatic and environmental gradient with large spatial and temporal variations of associated hydrological parameters. In this region, important hydrological components are integrated water vapor (IWV) and soil moisture. Both parameters can be monitored in parallel by using Global Navigation Satellite System - Reflectometry (GNSS-R) techniques. Soil moisture can furthermore be estimated using Synthetic Aperture Radar (SAR) data.</p><p>As part of International Research Training Group-StRATEGy project, our research aims at monitoring IWV and soil moisture with new station data in the Central Andes. According to the needs of the research, four independent GNSS ground stations and in-situ soil-moisture sensors were installed in spring 2019. Each station is located at different altitude along the climatic gradient and contains various quality GNSS receivers. It has been shown that high-quality receivers provide precise measurements, while low-quality receivers have not been widely tested for these applications. A goal of this project is the direct comparison of data quality from each site and receiver type. Additionally, soil moisture sensors were installed at each site. This set-up will help to evaluate the quality of the GNSS receivers. Moreover, the GNSS-based remote sensing approaches are directly compared to traditional Time-Domain Reflectometry (TDR) techniques. Meteorological data are used for studying the relation between the magnitude of precipitation events and soil moisture, as well as the time needed to spot a significant change in soil moisture after a precipitation event.</p><p>GNSS-R soil moisture estimations and in-situ measurements were compared with estimations derived from SAR data. More specifically, we used data from Sentinel-1 and Satélite Argentino de Observación COn Microondas (SAOCOM) missions. Sentinel-1 is a fully operational mission that uses C-band wavelengths, while SAOCOM relies on L-band wavelength, but is still in a calibration phase. We analyze both wavelengths and estimate the potential for soil-moisture measurements in the Argentinean Andes.</p>

scholarly journals Comparing the Ensemble and Extended Kalman Filters for in situ soil moisture assimilation with contrasting soil conditions

2015 ◽  
Vol 12 (8) ◽  
pp. 7353-7403
Author(s):  
D. Fairbairn ◽  
A. L. Barbu ◽  
J.-F. Mahfouf ◽  
J.-C. Calvet ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Sandy Soils ◽  
Soil Conditions ◽  
Surface Model ◽  
Random Component ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Correction Technique

Abstract. Two data assimilation methods are compared for their ability to produce a deterministic soil moisture analysis on the Météo-France land surface model: (i) SEKF, a Simplified Extended Kalman Filter, which uses a climatological background-error covariance, (ii) EnSRF, the Ensemble Square Root Filter, which uses an ensemble background-error covariance and approximates random forcing errors stochastically. The accuracy of the deterministic analysis is measured on 12 sites with in situ observations and various soil textures in Southwest France (SMOSMANIA network). In the experiments with real observations, the two methods perform similarly and improve on the open loop. Both methods suffer from incorrect linear assumptions which are particularly degrading to the analysis during water-stressed conditions: the EnSRF by a dry bias and the SEKF by an over-sensitivity of the model Jacobian between the surface and the root zone layers. These problems are less severe for sandy soils than clay soils because sandy soils are less sensitive to perturbations in the initial conditions. A simple bias correction technique is tested on the EnSRF. Although this reduces the bias, it also suppresses the ensemble spread, which degrades the analysis performance. However, the EnSRF flow-dependent background-error covariance evidently captures seasonal variability in the soil moisture errors and should exploit planned improvements in the model physics. Synthetic experiments demonstrate that when there is only a random component in the precipitation forcing errors, the correct stochastic representation of these errors enables the EnSRF to perform better than the SEKF. But in the real experiments the same rainfall error specification does not improve the EnSRF analysis. It is likely that the actual rainfall errors are underestimated and that other sources of errors could limit the usefulness of this information. More comprehensive ways of representing the rainfall errors are suggested, which might improve the EnSRF performance.

AUTOMATIC IN SITU DETERMINATION OF FIELD CAPACITY USING SOIL MOISTURE SENSORS

2011 ◽  
Vol 61 (3) ◽  
pp. 416-424 ◽  
Author(s):  
Scott Fazackerley ◽  
Ramon Lawrence
Keyword(s):  
Soil Moisture ◽  
Field Capacity ◽  
Soil Moisture Sensors

IN SITU SOIL FLUSHING - STUDIES ON REMEDIATION EFFICIENCY OF POLLUTED SANDY SOILS WITH ORGANIC ACIDS

2012 ◽  
Vol 11 (12) ◽  
pp. 2163-2168
Author(s):  
Alexandra-Dana Chitimus ◽  
Valentin Nedeff ◽  
Emilian Florin Mosnegutu ◽  
Mirela Panainte
Keyword(s):  
Organic Acids ◽  
Sandy Soils ◽  
Soil Flushing ◽  
Remediation Efficiency

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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